



"""simple docstring"""





import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


__lowerCAmelCase							:       Optional[Any]          =       [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int     ):


						'''simple docstring'''






						for pegasus_name, hf_name in PATTERNS:
												snake_case_ :			Optional[Any]            =      k.replace(__UpperCamelCase       ,			__UpperCamelCase     )
						return k
def 						__lowerCAmelCase    (		__UpperCamelCase   :      dict       ,			__UpperCamelCase   :      dict     ):


						'''simple docstring'''






						snake_case_ :			Any            =      DEFAULTS.copy()
						cfg_kwargs.update(__UpperCamelCase     )
						snake_case_ :			Union[str, Any]            =      PegasusConfig(**__UpperCamelCase     )
						snake_case_ :			Tuple            =      PegasusForConditionalGeneration(__UpperCamelCase     )
						snake_case_ :			Optional[int]            =      torch_model.model.state_dict()
						snake_case_ :			Dict            =      {}
						for k, v in tf_weights.items():
												snake_case_ :			str            =      rename_state_dict_key(__UpperCamelCase     )
												if new_k not in sd:
																		raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'     )

												if "dense" in k or "proj" in new_k:
																		snake_case_ :			Optional[Any]            =      v.T
												snake_case_ :			List[str]            =      torch.tensor(__UpperCamelCase       ,			dtype=sd[new_k].dtype     )
												assert v.shape == sd[new_k].shape, F'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
						# make sure embedding.padding_idx is respected
						snake_case_ :			str            =      torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]     )
						snake_case_ :			Tuple            =      mapping["""shared.weight"""]
						snake_case_ :			Optional[Any]            =      mapping["""shared.weight"""]
						snake_case_ :			List[str]            =      {k: torch.zeros_like(__UpperCamelCase     ) for k, v in sd.items() if k.endswith("""bias"""     ) and k not in mapping}
						mapping.update(**__UpperCamelCase     )
						snake_case_      ,		snake_case_ :			List[Any]            =      torch_model.model.load_state_dict(__UpperCamelCase       ,			strict=__UpperCamelCase     )
						snake_case_ :			str            =      [
						    k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
						]
						assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
						assert extra == [], F'no matches found for the following tf keys {extra}'
						return torch_model
def 						__lowerCAmelCase    (		__UpperCamelCase   :      List[Any]="./ckpt/aeslc/model.ckpt-32000"     ):


						'''simple docstring'''






						snake_case_ :			Dict            =      tf.train.list_variables(__UpperCamelCase     )
						snake_case_ :			Any            =      {}
						snake_case_ :			List[Any]            =      ["""Adafactor""", """global_step"""]
						for name, shape in tqdm(__UpperCamelCase       ,			desc="""converting tf checkpoint to dict"""     ):
												snake_case_ :			Dict            =      any(pat in name for pat in ignore_name     )
												if skip_key:
																		continue
												snake_case_ :			Union[str, Any]            =      tf.train.load_variable(__UpperCamelCase       ,			__UpperCamelCase     )
												snake_case_ :			List[Any]            =      array
						return tf_weights
def 						__lowerCAmelCase    (		__UpperCamelCase   :      str       ,			__UpperCamelCase   :      str     ):


						'''simple docstring'''






						snake_case_ :			Union[str, Any]            =      Path(__UpperCamelCase     ).parent.name
						snake_case_ :			str            =      task_specific_params[F'summarization_{dataset}']["""max_position_embeddings"""]
						snake_case_ :			int            =      PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""       ,			model_max_length=__UpperCamelCase     )
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(__UpperCamelCase     )

						# convert model
						snake_case_ :			Tuple            =      get_tf_weights_as_numpy(__UpperCamelCase     )
						snake_case_ :			Dict            =      task_specific_params[F'summarization_{dataset}']
						if dataset == "large":
												snake_case_ :			int            =      task_specific_params
						snake_case_ :			Optional[Any]            =      convert_pegasus(__UpperCamelCase       ,			__UpperCamelCase     )
						torch_model.save_pretrained(__UpperCamelCase     )
						snake_case_ :			str            =      torch_model.state_dict()
						sd.pop("""model.decoder.embed_positions.weight"""     )
						sd.pop("""model.encoder.embed_positions.weight"""     )
						torch.save(__UpperCamelCase       ,			Path(__UpperCamelCase     ) / """pytorch_model.bin"""     )


if __name__ == "__main__":
					__lowerCAmelCase							:       Optional[int]          =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
					parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
					__lowerCAmelCase							:       Dict          =       parser.parse_args()
					if args.save_dir is None:
										__lowerCAmelCase							:       List[str]          =       Path(args.tf_ckpt_path).parent.name
										__lowerCAmelCase							:       Tuple          =       os.path.join('''pegasus''', dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RobertaPreLayerNormForCausalLM""",
					    """RobertaPreLayerNormForMaskedLM""",
					    """RobertaPreLayerNormForMultipleChoice""",
					    """RobertaPreLayerNormForQuestionAnswering""",
					    """RobertaPreLayerNormForSequenceClassification""",
					    """RobertaPreLayerNormForTokenClassification""",
					    """RobertaPreLayerNormModel""",
					    """RobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRobertaPreLayerNormForCausalLM""",
					    """TFRobertaPreLayerNormForMaskedLM""",
					    """TFRobertaPreLayerNormForMultipleChoice""",
					    """TFRobertaPreLayerNormForQuestionAnswering""",
					    """TFRobertaPreLayerNormForSequenceClassification""",
					    """TFRobertaPreLayerNormForTokenClassification""",
					    """TFRobertaPreLayerNormMainLayer""",
					    """TFRobertaPreLayerNormModel""",
					    """TFRobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """FlaxRobertaPreLayerNormForCausalLM""",
					    """FlaxRobertaPreLayerNormForMaskedLM""",
					    """FlaxRobertaPreLayerNormForMultipleChoice""",
					    """FlaxRobertaPreLayerNormForQuestionAnswering""",
					    """FlaxRobertaPreLayerNormForSequenceClassification""",
					    """FlaxRobertaPreLayerNormForTokenClassification""",
					    """FlaxRobertaPreLayerNormModel""",
					    """FlaxRobertaPreLayerNormPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


